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pyecharts

pyecharts is a library to generate charts using Echarts. It simply provides the interface of 28+ kinds of charts between Echarts and Python. https://github.com/pyecharts/pyecharts

pip install pyecharts

pip install echarts-countries-pypkg

pip install echarts-china-provinces-pypkg

pip install echarts-china-cities-pypkg

http://nbviewer.jupyter.org/github/pyecharts/pyecharts-users-cases/blob/master/notebook-users-cases/notebook-user-cases.ipynb

Bar 

from pyecharts import Bar

attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图数据堆叠示例")
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True)

bar

bar = Bar("柱状图示例")
bar.add("商家A", attr, v1, is_stack=False)
bar.add("商家B", attr, v2, is_stack=False)

bar

bar = Bar("标记线和标记点示例")
bar.add("商家A", attr, v1, mark_point=["average"])
bar.add("商家B", attr, v2, mark_line=["min", "max"])

bar

bar = Bar("x 轴和 y 轴交换")
bar.add("商家A", attr, v1)
bar.add("商家B", attr, v2, is_convert=True)

bar

import random

attr = ["{}天".format(i) for i in range(30)]
v1 = [random.randint(1, 30) for _ in range(30)]
bar = Bar("Bar - datazoom - slider 示例")
bar.add("", attr, v1, is_label_show=True, is_datazoom_show=True)

bar

attr = ["{}天".format(i) for i in range(20)]
v1 = [random.randint(1, 20) for _ in range(20)]
bar = Bar("坐标轴标签旋转示例")
bar.add("", attr, v1, xaxis_interval=0, xaxis_rotate=30, yaxis_rotate=30)

bar

Bar3D 

from pyecharts import Bar3D

bar3d = Bar3D("3D 柱状图示例", width=1200, height=600)
x_axis = ["12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a", "11a",
          "12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p", "10p", "11p"]
y_aixs = ["Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday", "Sunday"]
data = [[0, 0, 5], [0, 1, 1], [0, 2, 0], [0, 3, 0], [0, 4, 0], [0, 5, 0], [0, 6, 0], [0, 7, 0],
        [0, 8, 0],[0, 9, 0], [0, 10, 0], [0, 11, 2], [0, 12, 4], [0, 13, 1], [0, 14, 1], [0, 15, 3],
        [0, 16, 4], [0, 17, 6], [0, 18, 4], [0, 19, 4], [0, 20, 3], [0, 21, 3], [0, 22, 2], [0, 23, 5],
        [1, 0, 7], [1, 1, 0], [1, 2, 0], [1, 3, 0], [1, 4, 0], [1, 5, 0], [1, 6, 0], [1, 7, 0], [1, 8, 0],
        [1, 9, 0], [1, 10, 5], [1, 11, 2], [1, 12, 2], [1, 13, 6], [1, 14, 9], [1, 15, 11], [1, 16, 6], [1, 17, 7],
        [1, 18, 8], [1, 19, 12], [1, 20, 5], [1, 21, 5], [1, 22, 7], [1, 23, 2], [2, 0, 1], [2, 1, 1],
        [2, 2, 0], [2, 3, 0], [2, 4, 0], [2, 5, 0], [2, 6, 0], [2, 7, 0], [2, 8, 0], [2, 9, 0], [2, 10, 3],
        [2, 11, 2], [2, 12, 1], [2, 13, 9], [2, 14, 8], [2, 15, 10], [2, 16, 6], [2, 17, 5], [2, 18, 5],
        [2, 19, 5], [2, 20, 7], [2, 21, 4], [2, 22, 2], [2, 23, 4], [3, 0, 7], [3, 1, 3], [3, 2, 0], [3, 3, 0],
        [3, 4, 0], [3, 5, 0], [3, 6, 0], [3, 7, 0], [3, 8, 1], [3, 9, 0], [3, 10, 5], [3, 11, 4], [3, 12, 7],
        [3, 13, 14], [3, 14, 13], [3, 15, 12], [3, 16, 9], [3, 17, 5], [3, 18, 5], [3, 19, 10], [3, 20, 6],
        [3, 21, 4], [3, 22, 4], [3, 23, 1], [4, 0, 1], [4, 1, 3], [4, 2, 0], [4, 3, 0], [4, 4, 0], [4, 5, 1],
        [4, 6, 0], [4, 7, 0], [4, 8, 0], [4, 9, 2], [4, 10, 4], [4, 11, 4], [4, 12, 2], [4, 13, 4], [4, 14, 4],
        [4, 15, 14], [4, 16, 12], [4, 17, 1], [4, 18, 8], [4, 19, 5], [4, 20, 3], [4, 21, 7], [4, 22, 3],
        [4, 23, 0], [5, 0, 2], [5, 1, 1], [5, 2, 0], [5, 3, 3], [5, 4, 0], [5, 5, 0], [5, 6, 0], [5, 7, 0],
        [5, 8, 2], [5, 9, 0], [5, 10, 4], [5, 11, 1], [5, 12, 5], [5, 13, 10], [5, 14, 5], [5, 15, 7], [5, 16, 11],
        [5, 17, 6], [5, 18, 0], [5, 19, 5], [5, 20, 3], [5, 21, 4], [5, 22, 2], [5, 23, 0], [6, 0, 1], [6, 1, 0],
        [6, 2, 0], [6, 3, 0], [6, 4, 0], [6, 5, 0], [6, 6, 0], [6, 7, 0], [6, 8, 0], [6, 9, 0], [6, 10, 1],
        [6, 11, 0], [6, 12, 2], [6, 13, 1], [6, 14, 3], [6, 15, 4], [6, 16, 0], [6, 17, 0], [6, 18, 0], [6, 19, 0],
        [6, 20, 1], [6, 21, 2], [6, 22, 2], [6, 23, 6]]

range_color = ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
               '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
bar3d.add("", x_axis, y_aixs, [[d[1], d[0], d[2]] for d in data], is_visualmap=True,
          visual_range=[0, 20], visual_range_color=range_color, grid3d_width=200, grid3d_depth=80)

bar3d

EffectScatter 

from pyecharts import EffectScatter

es = EffectScatter("动态散点图各种图形示例")
es.add("", [10], [10], symbol_size=20, effect_scale=3.5, effect_period=3, symbol="pin")
es.add("", [20], [20], symbol_size=12, effect_scale=4.5, effect_period=4,symbol="rect")
es.add("", [30], [30], symbol_size=30, effect_scale=5.5, effect_period=5,symbol="roundRect")
es.add("", [40], [40], symbol_size=10, effect_scale=6.5, effect_brushtype='fill',symbol="diamond")
es.add("", [50], [50], symbol_size=16, effect_scale=5.5, effect_period=3,symbol="arrow")
es.add("", [60], [60], symbol_size=6, effect_scale=2.5, effect_period=3,symbol="triangle")

es

Funnel 

from pyecharts import Funnel

attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
value = [20, 40, 60, 80, 100, 120]
funnel = Funnel("漏斗图示例")
funnel.add("商品", attr, value, is_label_show=True, label_pos="inside", label_text_color="#fff")

funnel

Gauge 

from pyecharts import Gauge

gauge = Gauge("仪表盘示例")
gauge.add("业务指标", "完成率", 66.66)

gauge

Geo 

from pyecharts import Geo

data = [
    ("海门", 9),("鄂尔多斯", 12),("招远", 12),("舟山", 12),("齐齐哈尔", 14),("盐城", 15),
    ("赤峰", 16),("青岛", 18),("乳山", 18),("金昌", 19),("泉州", 21),("莱西", 21),
    ("日照", 21),("胶南", 22),("南通", 23),("拉萨", 24),("云浮", 24),("梅州", 25),
    ("文登", 25),("上海", 25),("攀枝花", 25),("威海", 25),("承德", 25),("厦门", 26),
    ("汕尾", 26),("潮州", 26),("丹东", 27),("太仓", 27),("曲靖", 27),("烟台", 28),
    ("福州", 29),("瓦房店", 30),("即墨", 30),("抚顺", 31),("玉溪", 31),("张家口", 31),
    ("阳泉", 31),("莱州", 32),("湖州", 32),("汕头", 32),("昆山", 33),("宁波", 33),
    ("湛江", 33),("揭阳", 34),("荣成", 34),("连云港", 35),("葫芦岛", 35),("常熟", 36),
    ("东莞", 36),("河源", 36),("淮安", 36),("泰州", 36),("南宁", 37),("营口", 37),
    ("惠州", 37),("江阴", 37),("蓬莱", 37),("韶关", 38),("嘉峪关", 38),("广州", 38),
    ("延安", 38),("太原", 39),("清远", 39),("中山", 39),("昆明", 39),("寿光", 40),
    ("盘锦", 40),("长治", 41),("深圳", 41),("珠海", 42),("宿迁", 43),("咸阳", 43),
    ("铜川", 44),("平度", 44),("佛山", 44),("海口", 44),("江门", 45),("章丘", 45),
    ("肇庆", 46),("大连", 47),("临汾", 47),("吴江", 47),("石嘴山", 49),("沈阳", 50),
    ("苏州", 50),("茂名", 50),("嘉兴", 51),("长春", 51),("胶州", 52),("银川", 52),
    ("张家港", 52),("三门峡", 53),("锦州", 54),("南昌", 54),("柳州", 54),("三亚", 54),
    ("自贡", 56),("吉林", 56),("阳江", 57),("泸州", 57),("西宁", 57),("宜宾", 58),
    ("呼和浩特", 58),("成都", 58),("大同", 58),("镇江", 59),("桂林", 59),("张家界", 59),
    ("宜兴", 59),("北海", 60),("西安", 61),("金坛", 62),("东营", 62),("牡丹江", 63),
    ("遵义", 63),("绍兴", 63),("扬州", 64),("常州", 64),("潍坊", 65),("重庆", 66),
    ("台州", 67),("南京", 67),("滨州", 70),("贵阳", 71),("无锡", 71),("本溪", 71),
    ("克拉玛依", 72),("渭南", 72),("马鞍山", 72),("宝鸡", 72),("焦作", 75),("句容", 75),
    ("北京", 79),("徐州", 79),("衡水", 80),("包头", 80),("绵阳", 80),("乌鲁木齐", 84),
    ("枣庄", 84),("杭州", 84),("淄博", 85),("鞍山", 86),("溧阳", 86),("库尔勒", 86),
    ("安阳", 90),("开封", 90),("济南", 92),("德阳", 93),("温州", 95),("九江", 96),
    ("邯郸", 98),("临安", 99),("兰州", 99),("沧州", 100),("临沂", 103),("南充", 104),
    ("天津", 105),("富阳", 106),("泰安", 112),("诸暨", 112),("郑州", 113),("哈尔滨", 114),
    ("聊城", 116),("芜湖", 117),("唐山", 119),("平顶山", 119),("邢台", 119),("德州", 120),
    ("济宁", 120),("荆州", 127),("宜昌", 130),("义乌", 132),("丽水", 133),("洛阳", 134),
    ("秦皇岛", 136),("株洲", 143),("石家庄", 147),("莱芜", 148),("常德", 152),("保定", 153),
    ("湘潭", 154),("金华", 157),("岳阳", 169),("长沙", 175),("衢州", 177),("廊坊", 193),
    ("菏泽", 194),("合肥", 229),("武汉", 273),("大庆", 279)]

geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center",
width=1200, height=600, background_color='#404a59')
attr, value = geo.cast(data)
geo.add("", attr, value, visual_range=[0, 200], visual_text_color="#fff", symbol_size=15, is_visualmap=True)

geo

geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="#fff", title_pos="center", width=1200, height=600,
          background_color='#404a59')
attr, value = geo.cast(data)
geo.add("", attr, value, type="heatmap", is_visualmap=True, visual_range=[0, 300], visual_text_color='#fff')

geo

Graph 

from pyecharts import Graph

nodes = [{"name": "结点1", "symbolSize": 10},
         {"name": "结点2", "symbolSize": 20},
         {"name": "结点3", "symbolSize": 30},
         {"name": "结点4", "symbolSize": 40},
         {"name": "结点5", "symbolSize": 50},
         {"name": "结点6", "symbolSize": 40},
         {"name": "结点7", "symbolSize": 30},
         {"name": "结点8", "symbolSize": 20}]

links = []
for i in nodes:
    for j in nodes:
        links.append({"source": i.get('name'), "target": j.get('name')})
graph = Graph("关系图-力引导布局示例")
graph.add("", nodes, links, repulsion=8000)

graph

from pyecharts import Graph

nodes =  [
                { 'name': '乔布斯', 'symbolSize' : 30},
                { 'name': '丽萨-乔布斯','symbolSize' : 20},
                { 'name': '保罗-乔布斯','symbolSize' : 3},
                { 'name': '克拉拉-乔布斯','symbolSize' : 3},
                { 'name': '劳伦-鲍威尔','symbolSize' : 7},
                { 'name': '史蒂夫-沃兹尼艾克','symbolSize' : 5},
                { 'name': '奥巴马','symbolSize' : 8},
                { 'name': '比尔-盖茨','symbolSize' : 9},
                { 'name': '乔纳森-艾夫','symbolSize' : 4},
                { 'name': '蒂姆-库克','symbolSize' : 4},
                { 'name': '龙-韦恩','symbolSize' : 1},
            ]

relationship = [('丽萨-乔布斯', '乔布斯', 1),
                ('保罗-乔布斯', '乔布斯', 2),
                ('克拉拉-乔布斯', '乔布斯', 1),
                ('劳伦-鲍威尔', '乔布斯', 2),
                ('史蒂夫-沃兹尼艾克', '乔布斯', 3),
                ('奥巴马', '乔布斯', 1),
                ('比尔-盖茨', '乔布斯', 6),
                ('乔纳森-艾夫', '乔布斯', 1),
                ('蒂姆-库克', '乔布斯', 1),
                ('龙-韦恩', '乔布斯', 1),
                ('克拉拉-乔布斯', '保罗-乔布斯', 1),
                ('奥巴马', '保罗-乔布斯', 1),
                ('奥巴马', '克拉拉-乔布斯', 1),
                ('奥巴马', '劳伦-鲍威尔', 1),
                ('奥巴马', '史蒂夫-沃兹尼艾克', 1),
                ('比尔-盖茨', '奥巴马', 6),
                ('比尔-盖茨', '克拉拉-乔布斯', 1),
                ('蒂姆-库克', '奥巴马', 1)]

links = []
for i in relationship:
    links.append({"source": i[0], "target": i[1]})
graph = Graph("关系图-力引导布局示例2")
graph.add("", nodes, links, repulsion=200)

graph

import networkx as nx
from networkx.readwrite import json_graph
from pyecharts import Graph

g = nx.Graph()
g.add_node('G1', name='Gateway 1')
g.add_node('N2', name='Node 2')
g.add_node('N3', name='Node 3')
g.add_edge('G1', 'N2')
g.add_edge('G1', 'N3')
g_data = json_graph.node_link_data(g)
eg = Graph('设备最新拓扑图')
eg.add('Devices', nodes=g_data['nodes'], links=g_data['links'])
eg

ba=nx.barabasi_albert_graph(100,5)
g_data = json_graph.node_link_data(ba)
eg = Graph('BA Network')
eg.add('BA', nodes=g_data['nodes'], links=g_data['links'])
eg

G=nx.karate_club_graph()
g_data = json_graph.node_link_data(G)
eg = Graph('karate_club')
eg.add('karate_club', nodes=g_data['nodes'], links=g_data['links'])
eg

HeatMap 

from pyecharts import HeatMap

x_axis = ["12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a", "11a",
          "12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p", "10p", "11p"]
y_aixs = ["Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday", "Sunday"]
data = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = HeatMap()
heatmap.add("热力图直角坐标系", x_axis, y_aixs, data, is_visualmap=True,
            visual_text_color="#000", visual_orient='horizontal')

heatmap

Kline 

from pyecharts import Kline

v1 = [[2320.26, 2320.26, 2287.3, 2362.94], [2300, 2291.3, 2288.26, 2308.38],
      [2295.35, 2346.5, 2295.35, 2345.92], [2347.22, 2358.98, 2337.35, 2363.8],
      [2360.75, 2382.48, 2347.89, 2383.76], [2383.43, 2385.42, 2371.23, 2391.82],
      [2377.41, 2419.02, 2369.57, 2421.15], [2425.92, 2428.15, 2417.58, 2440.38],
      [2411, 2433.13, 2403.3, 2437.42], [2432.68, 2334.48, 2427.7, 2441.73],
      [2430.69, 2418.53, 2394.22, 2433.89], [2416.62, 2432.4, 2414.4, 2443.03],
      [2441.91, 2421.56, 2418.43, 2444.8], [2420.26, 2382.91, 2373.53, 2427.07],
      [2383.49, 2397.18, 2370.61, 2397.94], [2378.82, 2325.95, 2309.17, 2378.82],
      [2322.94, 2314.16, 2308.76, 2330.88], [2320.62, 2325.82, 2315.01, 2338.78],
      [2313.74, 2293.34, 2289.89, 2340.71], [2297.77, 2313.22, 2292.03, 2324.63],
      [2322.32, 2365.59, 2308.92, 2366.16], [2364.54, 2359.51, 2330.86, 2369.65],
      [2332.08, 2273.4, 2259.25, 2333.54], [2274.81, 2326.31, 2270.1, 2328.14],
      [2333.61, 2347.18, 2321.6, 2351.44], [2340.44, 2324.29, 2304.27, 2352.02],
      [2326.42, 2318.61, 2314.59, 2333.67], [2314.68, 2310.59, 2296.58, 2320.96],
      [2309.16, 2286.6, 2264.83, 2333.29], [2282.17, 2263.97, 2253.25, 2286.33],
      [2255.77, 2270.28, 2253.31, 2276.22]]

kline = Kline("K 线图示例")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)], v1)

kline

Line 

from pyecharts import Line

attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 10, 100]
v2 = [55, 60, 16, 20, 15, 80]
line = Line("折线图示例")
line.add("商家A", attr, v1, mark_point=["average"])
line.add("商家B", attr, v2, is_smooth=True, mark_line=["max", "average"])

line

line = Line("折线图-面积图示例")
line.add("商家A", attr, v1, is_fill=True, line_opacity=0.2, area_opacity=0.4, symbol=None)
line.add("商家B", attr, v2, is_fill=True, area_color='#000', area_opacity=0.3, is_smooth=True)

line

Line3D 

from pyecharts import Line3D

import math
_data = []
for t in range(0, 25000):
    _t = t / 1000
    x = (1 + 0.25 * math.cos(75 * _t)) * math.cos(_t)
    y = (1 + 0.25 * math.cos(75 * _t)) * math.sin(_t)
    z = _t + 2.0 * math.sin(75 * _t)
    _data.append([x, y, z])

range_color = ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
               '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
line3d = Line3D("3D 折线图示例", width=1200, height=600)
line3d.add("", _data, is_visualmap=True, visual_range_color=range_color, visual_range=[0, 30],
           is_grid3d_rotate=True, grid3d_rotate_speed=180)

line3d

Liquid 

from pyecharts import Liquid

liquid = Liquid("水球图示例")
liquid.add("Liquid", [0.6, 0.5, 0.4, 0.3], is_liquid_outline_show=False)

liquid

Map 

from pyecharts import Map

value = [155, 10, 66, 78]
attr = ["福建", "山东", "北京", "上海"]
map = Map("全国地图示例", width=1200, height=600)
map.add("", attr, value, maptype='china')

map

from pyecharts import Map

value = [20, 90, 453, 77, 65]
attr = ['南京市', '镇江市', '扬州市', '徐州市', '苏州市']
map = Map("江苏省地图示例", width=1200, height=600)
map.add("", attr, value, maptype='江苏', is_visualmap=True, visual_text_color='#000')

map

#https://baike.baidu.com/item/沧州
data = [('运河区', 109.92), ('新华区', 109.47), ('泊头市', 1006.5),
 ('任丘市', 1023.0), ('黄骅市', 1544.7), ('河间市', 1333.0),
 ('沧县', 1104.0), ('青县', 968.0), ('东光县', 730.0),
 ('海兴县', 915.1), ('盐山县', 796.0), ('肃宁县', 525.0),
 ('南皮县', 794.0), ('吴桥县', 600.0), ('献县', 1191.0),
 ('孟村回族自治县', 387.0)]

districts, areas = zip(*data)

map = Map("沧州市图例－各区面积", width=1200, height=600)
map.add("", districts, areas, maptype='沧州', is_visualmap=True, visual_range=[min(areas), max(areas)],
        visual_text_color='#000', is_map_symbol_show=False, is_label_show=True)
map

value = [1, 100]
attr = ['Gujarat', 'Tamil Nadu']
map = Map('India', width=800, height=600)
map.add('', attr, value, maptype=u'印度', is_visualmap=True, visual_text_color="#000")
map

value = [95.1, 23.2, 43.3, 66.4, 88.5]
attr= ["China", "Canada", "Brazil", "Russia", "United States"]
map = Map("世界地图示例", width=1200, height=600)
map.add("", attr, value, maptype="world", is_visualmap=True, visual_text_color='#000')

map

Parallel 

from pyecharts import Parallel

schema = ["data", "AQI", "PM2.5", "PM10", "CO", "NO2"]
data = [[1, 91, 45, 125, 0.82, 34],
        [2, 65, 27, 78, 0.86, 45,],
        [3, 83, 60, 84, 1.09, 73],
        [4, 109, 81, 121, 1.28, 68],
        [5, 106, 77, 114, 1.07, 55],
        [6, 109, 81, 121, 1.28, 68],
        [7, 106, 77, 114, 1.07, 55],
        [8, 89, 65, 78, 0.86, 51, 26],
        [9, 53, 33, 47, 0.64, 50, 17],
        [10, 80, 55, 80, 1.01, 75, 24],
        [11, 117, 81, 124, 1.03, 45]]
parallel = Parallel("平行坐标系-默认指示器")
parallel.config(schema) 
parallel.add("parallel", data, is_random=True)

parallel

Pie 

from pyecharts import Pie

attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图示例")
pie.add("", attr, v1, is_label_show=True)

pie

attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
v2 = [19, 21, 32, 20, 20, 33]
pie = Pie("饼图-玫瑰图示例", title_pos='center', width=900)
pie.add("商品A", attr, v1, center=[25, 50], is_random=True, radius=[30, 75], rosetype='radius')
pie.add("商品B", attr, v2, center=[75, 50], is_random=True, radius=[30, 75], rosetype='area',
        is_legend_show=False, is_label_show=True)

pie

Polar 

from pyecharts import Polar

import random
data = [(i, random.randint(1, 100)) for i in range(101)]
polar = Polar("极坐标系-散点图示例")
polar.add("", data, boundary_gap=False, type='scatter', is_splitline_show=False,
          area_color=None, is_axisline_show=True)

polar

radius = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
polar = Polar("极坐标系-堆叠柱状图示例", width=1200, height=600)
polar.add("A", [1, 2, 3, 4, 3, 5, 1], radius_data=radius, type='barRadius', is_stack=True)
polar.add("B", [2, 4, 6, 1, 2, 3, 1], radius_data=radius, type='barRadius', is_stack=True)
polar.add("C", [1, 2, 3, 4, 1, 2, 5], radius_data=radius, type='barRadius', is_stack=True)

polar

Radar 

from pyecharts import Radar

schema = [ 
    ("销售", 6500), ("管理", 16000), ("信息技术", 30000), ("客服", 38000), ("研发", 52000), ("市场", 25000)]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar()
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False, legend_selectedmode='single')

radar

Scatter 

help(scatter)

Help on Scatter in module pyecharts.charts.scatter object:

class Scatter(pyecharts.chart.Chart)
 |  <<< 散点图 >>>
 |  
 |  直角坐标系上的散点图可以用来展现数据的 x，y 之间的关系，如果数据项有多个维度，
 |  可以用颜色来表现，利用 geo 组件。
 |  
 |  Method resolution order:
 |      Scatter
 |      pyecharts.chart.Chart
 |      pyecharts.base.Base
 |      builtins.object
 |  
 |  Methods defined here:
 |  
 |  __init__(self, title='', subtitle='', **kwargs)
 |             :param title:
 |                 主标题文本，支持 
 |      换行，默认为 ""
 |             :param subtitle:
 |                 副标题文本，支持 
 |      换行，默认为 ""
 |             :param width:
 |                 画布宽度，默认为 800（px）
 |             :param height:
 |                 画布高度，默认为 400（px）
 |             :param title_pos:
 |                 标题距离左侧距离，默认为'left'，有'auto', 'left', 'right',
 |                 'center'可选，也可为百分比或整数
 |             :param title_top:
 |                 标题距离顶部距离，默认为'top'，有'top', 'middle', 'bottom'可选，
 |                 也可为百分比或整数
 |             :param title_color:
 |                 主标题文本颜色，默认为 '#000'
 |             :param subtitle_color:
 |                 副标题文本颜色，默认为 '#aaa'
 |             :param title_text_size:
 |                 主标题文本字体大小，默认为 18
 |             :param subtitle_text_size:
 |                 副标题文本字体大小，默认为 12
 |             :param background_color:
 |                 画布背景颜色，默认为 '#fff'
 |             :param page_title:
 |                 指定生成的 html 文件中 <title> 标签的值。默认为 'Echarts'
 |             :param renderer:
 |                 指定使用渲染方式，有 'svg' 和 'canvas' 可选，默认为 'canvas'。
 |                 3D 图仅能使用 'canvas'。
 |  
 |  add(self, *args, **kwargs)
 |      `add()` 方法只是用于提供自动参数补全
 |  
 |  draw(self, path, color=None)
 |      将图片上的像素点转换为数组，如 color 为（255,255,255）时只保留非白色像素点的
 |      坐标信息返回两个 k_lst, v_lst 两个列表刚好作为散点图的数据项
 |      
 |      :param path:
 |          转换图片的地址
 |      :param color:
 |          所要排除的颜色
 |      :return:
 |          转换后的数组
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from pyecharts.base.Base:
 |  
 |  get_js_dependencies(self)
 |      声明所有的 js 文件路径
 |  
 |  print_echarts_options(self)
 |      打印输出图形所有配置项
 |  
 |  render(self, path='render.html', template_name='simple_chart.html', object_name='chart', extra_context=None)
 |  
 |  render_embed(self)
 |      渲染图表的所有配置项，为 web pages 服务，不过需先提供
 |      所需要的js 依赖文件
 |  
 |  show_config(self)
 |      打印输出图形所有配置项
 |  
 |  ----------------------------------------------------------------------
 |  Static methods inherited from pyecharts.base.Base:
 |  
 |  cast(seq)
 |      转换数据序列，将带字典和元组类型的序列转换为 k_lst,v_lst 两个列表
 |      
 |      元组列表
 |          [(A1, B1), (A2, B2), ...] -->
 |              k_lst[ A[i1, i2...] ], v_lst[ B[i1, i2...] ]
 |      字典列表
 |          [{A1: B1}, {A2: B2}, ...] -->
 |              k_lst[ A[i1, i2...] ], v_lst[ B[i1, i2...] ]
 |      字典
 |          {A1: B1, A2: B2, ...} -- >
 |              k_lst[ A[i1, i2...] ], v_lst[ B[i1, i2...] ]
 |      
 |      :param seq:
 |          待转换的序列
 |      :return:
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from pyecharts.base.Base:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  chart_id
 |  
 |  js_dependencies
 |  
 |  options
 |  
 |  page_title


from pyecharts import Scatter

v1 = [10, 20, 30, 40, 50, 60]
v2 = [10, 20, 30, 40, 50, 60]
scatter = Scatter("散点图示例")
scatter.add("A", v1, v2)
#scatter.add("B", v1[::-1], v2)

scatter

scatter = Scatter("散点图示例")
scatter.add("A", ["a", "b", "c", "d", "e", "f"], v2)
scatter.add("B", ["a", "b", "c", "d", "e", "f"], v1[::-1], xaxis_type="category")

scatter

Scatter3D 

from pyecharts import Scatter3D

import random
data = [[random.randint(0, 100), random.randint(0, 100), random.randint(0, 100)] for _ in range(80)]
range_color = ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
               '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
scatter3D = Scatter3D("3D 散点图示例", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=range_color)

scatter3D

WordCloud 

from pyecharts import WordCloud

name = ['Sam S Club', 'Macys', 'Amy Schumer', 'Jurassic World', 'Charter Communications',
        'Chick Fil A', 'Planet Fitness', 'Pitch Perfect', 'Express', 'Home', 'Johnny Depp',
        'Lena Dunham', 'Lewis Hamilton', 'KXAN', 'Mary Ellen Mark', 'Farrah Abraham',
        'Rita Ora', 'Serena Williams', 'NCAA baseball tournament', 'Point Break']
value = [10000, 6181, 4386, 4055, 2467, 2244, 1898, 1484, 1112, 965, 847, 582, 555,
         550, 462, 366, 360, 282, 273, 265]
wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", name, value, word_size_range=[20, 100])

wordcloud

import jieba.analyse
import numpy as np

with open('../data/gov_reports1954-2017.txt', 'r') as f:
    reports = f.readlines()

txt = reports[-1]
tf = jieba.analyse.extract_tags(txt, topK=100, withWeight=True)
name, value = np.array(tf).T

wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", name, value, word_size_range=[20, 100])

wordcloud

Grid 

from pyecharts import Bar, Line, Scatter, EffectScatter, Grid  

attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%")
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True, legend_pos="80%")

line = Line("折线图示例")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
         mark_line=["average"], legend_pos="20%")
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0], effect_scale=6,
        legend_top="50%", legend_pos="20%")

grid = Grid()
grid.add(bar, grid_bottom="60%", grid_left="60%")
grid.add(line, grid_bottom="60%", grid_right="60%")
grid.add(scatter, grid_top="60%", grid_left="60%")
grid.add(es, grid_top="60%", grid_right="60%")

grid

Save html files 

grid.render(path = 'grid.html')
#help(grid.render)

Overlap 

from pyecharts import Bar, Line, Overlap

attr = ['A', 'B', 'C', 'D', 'E', 'F']
v1 = [10, 20, 30, 40, 50, 60]
v2 = [38, 28, 58, 48, 78, 68]
bar = Bar("Line - Bar 示例")
bar.add("bar", attr, v1)
line = Line()
line.add("line", attr, v2)

overlap = Overlap()
overlap.add(bar)
overlap.add(line)

overlap

Page 

from pyecharts import Page, Scatter, EffectScatter
page = Page()
v1 = [10, 20, 30, 40, 50, 60]
v2 = [10, 20, 30, 40, 50, 60]
scatter = Scatter("散点图示例")
scatter.add("A", v1, v2)
scatter.add("B", v1[::-1], v2)

page.add(scatter)

es = EffectScatter("动态散点图示例", title_top="50%")
es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0], effect_scale=6,
        legend_top="50%", legend_pos="20%")
page.add(es)

page

Timeline 

from pyecharts import Bar, Timeline

from random import randint

attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
bar_1 = Bar("2012 年销量", "数据纯属虚构")
bar_1.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_1.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_1.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_1.add("冬季", attr, [randint(10, 100) for _ in range(6)])

bar_2 = Bar("2013 年销量", "数据纯属虚构")
bar_2.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_2.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_2.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_2.add("冬季", attr, [randint(10, 100) for _ in range(6)])

bar_3 = Bar("2014 年销量", "数据纯属虚构")
bar_3.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_3.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_3.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_3.add("冬季", attr, [randint(10, 100) for _ in range(6)])

bar_4 = Bar("2015 年销量", "数据纯属虚构")
bar_4.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_4.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_4.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_4.add("冬季", attr, [randint(10, 100) for _ in range(6)])

bar_5 = Bar("2016 年销量", "数据纯属虚构")
bar_5.add("春季", attr, [randint(10, 100) for _ in range(6)])
bar_5.add("夏季", attr, [randint(10, 100) for _ in range(6)])
bar_5.add("秋季", attr, [randint(10, 100) for _ in range(6)])
bar_5.add("冬季", attr, [randint(10, 100) for _ in range(6)], is_legend_show=True)

timeline = Timeline(is_auto_play=True, timeline_bottom=0)
timeline.add(bar_1, '2012 年')
timeline.add(bar_2, '2013 年')
timeline.add(bar_3, '2014 年')
timeline.add(bar_4, '2015 年')
timeline.add(bar_5, '2016 年')

timeline

More

https://github.com/pyecharts/pyecharts/tree/master/test

from pyecharts import ThemeRiver

data = [
    ['2015/11/08', 10, 'DQ'], ['2015/11/09', 15, 'DQ'],
    ['2015/11/10', 35, 'DQ'], ['2015/11/14', 7, 'DQ'],
    ['2015/11/15', 2, 'DQ'], ['2015/11/16', 17, 'DQ'],
    ['2015/11/17', 33, 'DQ'], ['2015/11/18', 40, 'DQ'],
    ['2015/11/19', 32, 'DQ'], ['2015/11/20', 26, 'DQ'],
    ['2015/11/21', 35, 'DQ'], ['2015/11/22', 40, 'DQ'],
    ['2015/11/23', 32, 'DQ'], ['2015/11/24', 26, 'DQ'],
    ['2015/11/25', 22, 'DQ'], ['2015/11/08', 35, 'TY'],
    ['2015/11/09', 36, 'TY'], ['2015/11/10', 37, 'TY'],
    ['2015/11/11', 22, 'TY'], ['2015/11/12', 24, 'TY'],
    ['2015/11/13', 26, 'TY'], ['2015/11/14', 34, 'TY'],
    ['2015/11/15', 21, 'TY'], ['2015/11/16', 18, 'TY'],
    ['2015/11/17', 45, 'TY'], ['2015/11/18', 32, 'TY'],
    ['2015/11/19', 35, 'TY'], ['2015/11/20', 30, 'TY'],
    ['2015/11/21', 28, 'TY'], ['2015/11/22', 27, 'TY'],
    ['2015/11/23', 26, 'TY'], ['2015/11/24', 15, 'TY'],
    ['2015/11/25', 30, 'TY'], ['2015/11/26', 35, 'TY'],
    ['2015/11/27', 42, 'TY'], ['2015/11/28', 42, 'TY'],
    ['2015/11/08', 21, 'SS'], ['2015/11/09', 25, 'SS'],
    ['2015/11/10', 27, 'SS'], ['2015/11/11', 23, 'SS'],
    ['2015/11/12', 24, 'SS'], ['2015/11/13', 21, 'SS'],
    ['2015/11/14', 35, 'SS'], ['2015/11/15', 39, 'SS']
]
tr = ThemeRiver("主题河流图示例图")
tr.add(['DQ', 'TY', 'SS', 'QG', 'SY', 'DD'], data, is_label_show=True)
tr

from pyecharts import Boxplot


boxplot = Boxplot("箱形图")
x_axis = ['expr1', 'expr2', 'expr3', 'expr4', 'expr5']
y_axis = [
    [850, 740, 900, 1070, 930, 850, 950, 980, 980, 880,
     1000, 980, 930, 650, 760, 810, 1000, 1000, 960, 960],
    [960, 940, 960, 940, 880, 800, 850, 880, 900, 840,
     830, 790, 810, 880, 880, 830, 800, 790, 760, 800],
    [880, 880, 880, 860, 720, 720, 620, 860, 970, 950,
     880, 910, 850, 870, 840, 840, 850, 840, 840, 840],
    [890, 810, 810, 820, 800, 770, 760, 740, 750, 760,
     910, 920, 890, 860, 880, 720, 840, 850, 850, 780],
    [890, 840, 780, 810, 760, 810, 790, 810, 820, 850,
     870, 870, 810, 740, 810, 940, 950, 800, 810, 870]
]
_yaxis = boxplot.prepare_data(y_axis)
boxplot.add("boxplot", x_axis, _yaxis)
boxplot